Traffic information detection systems using existing video image detection systems (VIDS) have many advantages. For example, such detection systems typically use video cameras as sensors, thus having wide area detection capabilities. Usually, one camera can cover several traffic lanes, which is difficult to achieve using any other sensors like radar or is conductive loops. While images generated by video camera sensors allow for efficient detection of shock waves and other spatial traffic parameters, such as density, queue lengths, and speed profiles, it is typically not the case for images generated by other conventional means. In addition, the VIDS provides ancillary information such as traffic on road shoulders, stopped vehicles, changed lanes, speed variations between vehicles, and traffic slowdowns in the other direction. As the size of camera sensors decreases and processing capabilities of processors increase, it is more and more common to employ traffic information detection systems with VIDS.
FIG. 1 illustrates a block diagram of the prior art traffic information detection system using VIDS. One disadvantage with this prior art traffic information detection system is that as shown in the figure, all the video sensors need to transfer video stream data from the fields where the sensors are located to the traffic management center. This makes the video images acquired by each of the camera sensors subject to video compression when transmitted to the traffic management center. In this case, expensive real-time decoders are used to decompress the compressed video images at the receiving end, thus making the prior art traffic detection system relatively more expensive. Another contributing factor to the relatively expensive traffic detection system is that due to the relatively high code rate of the compressed video stream, a large amount of network bandwidths are required to transmit even the compressed video stream.
Another disadvantage of the prior art traffic detection system is that the sensors cannot identify traffic lanes automatically, thus requiring operating personnel manually obtain lane information from the sample images during installation. The lane information is also transmitted back to the traffic management center for subsequent detection. For a traffic is detection system that includes thousands of sensors, such activities involve an enormous amount of labor. Moreover, once the position or orientation of a sensor is changed, the lane information will have to be re-configured; otherwise erroneous detection results will be generated. Thus, the lack of automatic lane identification capability of the prior art sensors has brought great inconvenience to traffic information detections.